Neurogrid's Electronic Brain Controls Robotics

PORTLAND, Ore. -- Neurogrid is a human brain emulator based on a mixed-signal application specific integrated circuit (ASIC) that emulates the real neuron networks of the human brain. Composed of arrays of Neurocore chips, the Stanford University Brain in Silicon project currently uses 16 Neurocore chips to emulate a million neurons with six billion synaptic interconnections among them. This synthetic brain operates in realtime at a neuron firing rate typical or biological brains -- 10 spikes per second per neuron. The project has secured $10 million in funding to create a chip implantable in the brain to control prosthetics and to upgrade the current 180 nanometer CMOS process to 28 nanometer, which should allow all million neurons and six billion synapses to be fabricated on a single chip. Going to a single chip implementation should also bring down the cost of a Neurogrid system from about $40,000 today to a goal of $400. The group is also working on compiler software to make it easier to configure a Neurogrid system.

"We have recently gone beyond scientific demonstrations to real applications where we use Neurogrid to control a robotic arm in realtime with a goal of controlling a prosthetic limb," bioengineer Kwabena Boahen told EETimes in an interview. "We have $10 million in funding right now from the National Institute of Health [NIH] and Office of Naval Research [ONR] to use the neuromorphic chips as very low power computing devices. A $5 million NIH Pioneer Award, over five years, aims to implant a chip inside the brain to actually control a prosthetic limb. And the other $5 million from ONR, over five years, is to scale up the chip to several million neurons to control a drone."

The Neurogrid board uses 16 Neurocore chips to simulate one million neurons with six billion synaptic connections.
(SOURCE: Stanford University)

The Neurogrid today uses 16 NeuroCore chips laid out in a tree-network on a printed circuit board (PCB), each with 65,536 neurons arranged in a 256-by-256 array measuring 11.9-by-13.9 millimeters. The on-chip analog circuitry reduces the board's power consumption to just three watts. The neurons are interconnected with brain-like synaptic circuits that emulate the ion channel and spiking voltage pulse architecture of real brains. Boahen is also collaborating with EE professor Krishna Shenoy, a colleague at Stanford's Bio-X center, to translate brain signals into usable control signals for prosthetic limbs. The Neurogrid system could someday control not only prosthetic limbs, but a complete humanoid robot.

"NeuroGrid is a system that can simulate a million neurons in realtime and that kind of power translates to teraflops of computing power that is only otherwise available on supercomputers. And these supercomputers burn on the order of a million watts -- this is just to simulate a million neurons. Your brain has a hundred billion [neurons] and is using only about 20 Watts," said Boahen in a video. "What we've done is take the same transistors, and instead of using them as digital logic, we used them actually as analog circuits. And we tried to develop the circuits at a level of voltage that corresponds to the level of voltage inside a cell, the level of a change corresponds to the amount of synaptic input, so we have done all this and saved a lot or power."

The Neurocore tile contains four synaptic population circuits, four ion channel population circuits, a dendritic compartment circuit and a somatic compartment circuit. A Neurocore has 256-by-256 of these tiles.

Projects to map out the detailed functions of the human brain are being undertaken by IBM in its Defense Advanced Research Project (DARPA) sponsored Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program, and by the European Union it its Blue Brain project. The Neurogrid project differs, however, in its concentrated effort to simplify the simulation process with a goal of ultra-low-power operation, resulting in the under three-watt power consumption of Stanford's NeuroGrid. Despite is simplification of its brain emulating abilities, each NeuroCore chip has 61 graded and 18 binary programmable parameters allowing it to model several different spiking and interaction patterns in the brain.

"This becomes viable [in applications] for, say quadriplegics, or people who have lot control over a limb for some reason. You can bypass where ever that damage is, by taking signals directly from the cortex and using them to control some kind of robotic limb," said doctoral candidate San Fok in the video. "The low power aspect is required for the clinical viability, because it needs to be low-power to be implantable -- NeuroGrid and neuromorphic technology enables that."

Stanford University bioengineer Kwabena Boahen's Neurogrid simulates one million neurons and six billion synaptic connections which will be used in robotics and advanced "smart" prosthetic limbs. (Credit: Kurt Hickman)

Boahen and colleagues recently revealed many of the details of the Neurocore chip Neurogrid architecture in a special issue of the Proceedings of the IEEE titled Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations which is free to all IEEE members. The article also surveys efforts to explore and simulate the brain in the U.S. (the Brain Research through Advancing Innovative Neurotechnologies project abbreviated BRAIN) and Europe (the Human Brain Project). It also makes comparisons with other projects to create brain emulating chips, namely IBM’s GoldenGate chip for DARPA's SyNAPSE project, the University of Manchester’s SpiNNaker project and Heidelberg University’s BrainScales project using its High Input Count Analog Neural Network (HICANN).

>> It seems that a large portion of the savings come from being application specific and using analog computation.

Yes it is analog computation but a different beast of analog computation. You are looking at weak inversion biasing of transistors which gives you low power but you have to deal with lots of noise issues in your design.

>> But since Google has already simulated a 10 billion simplified neuron system on only 16 GPU's and used it fr computer vision one has to wonder whether simplified neurons will do

Google did that using FPGA and massive computing power which adds not a lot of value when compared to the real brain. We are not talking of having a datacenter to power your brain. We want something less power hungry and efficient that is close to the natural one. That is the innovation in Kwabena's work.

>> 100,000 times more energy efficient, amazing...congrats Kwabena! maybe it is time for you to give a talk at emerging technologies conference

Kwabena is such a big fella to get invitation this way. He is truly leading the neuromorphic nexus. The path to human immortality could start this small as after cloning the brain in a circuit, the next phase will be making it to replace the one we have in case one needs a better one.

The relatively low power consumption is not quite as incredible as it sounds. It seems that a large portion of the savings come from being application specific and using analog computation.

That is not meant to diminish the achievement. There seems to be significant potential use for analog (and other approximate) computation and special purpose design. Not only could the Neurogrid device be useful in studying biological systems, but the project's work on hardware and software design might help advance use of similar systems for other areas.

This is definetly asn interesting project, and it will surely be useful for learning more about the brain.

But since Google has already simulated a 10 billion simplified neuron system on only 16 GPU's and used it fr computer vision one has to wonder whether simplified neurons will do , or we really need complex neurons for engineering applications ?